Research

We derive generalized bounds on conditional expected excess returns that can be computed from option prices. The generalized lower bound (GLB) may serve as an expected excess return proxy for individual and basket-type assets, is conditionally tight, accounts for the entire risk-neutral distribution of returns, and outperforms existing variance-based models in out-of-sample predictions. Bounds calibrated to realized returns correspond to reasonable risk aversion and prudence. On average, expected stock returns given by the bounds decrease on even weeks of the FOMC cycle. Cross-sectional tests deliver a reasonable market risk premium. 

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In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants. 

This paper analyzes the extent to which nonprofessional social media investment analysts (SMAs) form correct beliefs about stock returns. Contrary to the wisdom-of-the-crowd view, over half of the SMAs are skilled and express beliefs that correctly align with future returns. There is substantial skill heterogeneity among the SMAs: some 13% high-type SMAs produce a one-week three-factor alpha of 61 bps, while the remaining 87% generate only 6 bps. Firm and industry specializations are the high type's distinctive traits. Although SMAs extrapolate and herd, their expectations are not systematically wrong. Consistent with theory, the extrapolation and herding intensities depend on SMAs' skills.

Forecasters who are optimistic about an asset react to negative news by shifting their optimistic expectations to a longer forecast horizon. To document this novel pattern of optimism shifting in belief updating, we rely on CAPS, a social-finance platform offering the unique opportunity to observe individuals' beliefs about stocks alongside their chosen forecast horizon. Additional analysis indicates that optimism shifting leads to large underperformance, and it is consistent with forecasters’ motivation to retain optimistic beliefs about their skill (confidence channel), the value of their financial assets (financial-stakes channel), and the value of their accrued knowledge about an asset (intangible-stakes channel).

We propose a novel high-frequency measure of sovereign default risk that can be used when traditional metrics like CDS spreads are unavailable. The measure exploits the information in news text, can be computed in real-time for any country, and is highly informative about sovereign default risk. It predicts sovereign CDS spreads, rating downgrades, and realized defaults over long horizons. Consistent with theories on sovereign risk spillovers, an increase in the index is associated with higher firm default probability, default protection cost, and lower equity valuation. The measure is valuable for equity market-timing, and its informativeness is driven by macroeconomic concerns.

Investors fear that surging volumes in short-term, especially same-day expiry (0DTE), options can destabilize markets by propagating large price jumps. Contrary to the intuition that 0DTE sellers predominantly generate delta-hedging flows that aggravate market moves, high open interest gamma in 0DTEs does not propagate past volatility. 0DTEs and underlying markets have become more integrated over time, leading to a marginally stronger link between the index volatility and 0DTE trading. Nonetheless, intraday 0DTE trading volume shocks do not amplify recent past index returns, inconsistent with the view that 0DTEs market growth intensifies market fragility.

We use empirical Bayes (EB) to mine data on 140,000 long-short strategies constructed from accounting ratios, past returns, and ticker symbols. This "high-throughput asset pricing" produces out-of-sample performance comparable to strategies in top finance journals. But unlike the published strategies, the data-mined strategies are free of look-ahead bias. EB predicts that high returns are concentrated in accounting strategies, small stocks, and pre-2004 samples, consistent with limited attention theories. The intuition is seen in the cross-sectional distribution of t-stats, which is far from the null for equal-weighted accounting strategies. High-throughput methods provide a rigorous, unbiased method for documenting asset pricing facts.

The capacity to aggregate information from diverse perspectives has positioned social finance forums as a potent source of signals that shape investors’ beliefs and actions. We investigate how investors react to the information provided by male and female non-professional analysts on social platforms and the financial market consequences. Although male and female contributors exhibit similar informativeness and skills, female-authored perspectives receive significantly lower engagement, lower trust, and higher disagreement from platform users. The higher disagreement is subsequently associated with higher abnormal trading volume and lower price efficiency. Additional analysis indicates that the platform consensus becomes less informative about future cash flows when female contributors quit due to their less favorable engagement.

We show that an increase in stock return exposure to media attention to narratives, measured with standard methods for extracting topic attention from news text, leads to a lower stock price informativeness about future fundamentals. Empirically, narrative exposure explains over 86% of idiosyncratic variance in the cross-section, and both narrative exposure and non-systematic information channels—idiosyncratic variance and variance related to public information—decrease stock price informativeness. Moreover, stocks with high narrative exposure demonstrate elevated trading volume. To rationalize these results, we develop a theoretical model based on investor disagreement stemming from differential interpretations of media narratives.

We study learning and uncertainty under the factor investing paradigm using an endogenous information model with correlated assets. As investors shift attention from firms towards systematic risk factors, stock prices become less informative, increasing systematic uncertainty and incentivizing learning about the systematic risk. This learning complementarity leads to multiple regimes in systematic uncertainty and attention allocation. We specify and estimate a model-based, forward-looking measure of attention to systematic versus firm-level information. Consistent with the model, the measure follows a regime-switching process. The high-level regime is linked to lower stock price sensitivity to firm-specific information and a higher systematic risk concentration.